startup-metrics-framework
Enables your agent to track and optimize key metrics for startups. It's useful for understanding growth, customer acquisition costs, and the overall health of the business.
What it does
This guide helps startups understand and track important growth metrics. It explains how to calculate and optimize key performance indicators (KPIs) for different business types, like marketplaces and software companies, from their early stages through larger funding rounds.
When to use it
- ✓When defining which numbers are important for your startup.
- ✓When calculating customer acquisition cost (CAC) and customer lifetime value (LTV).
- ✓When preparing reports for investors or the board.
Example of what to ask your AI
“How can I calculate customer retention rate for my app startup?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The AI accesses information about business metrics like growth, engagement, and finances. It uses this information to explain how to calculate and interpret different indicators, offering examples and benchmarks.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add wshobson/agents --skill startup-metrics-frameworkOr download just the file:
curl -L https://raw.githubusercontent.com/wshobson/agents/main/plugins/startup-business-analyst/skills/startup-metrics-framework/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Startup Metrics Framework
Comprehensive guide to tracking, calculating, and optimizing key performance metrics for different startup business models from seed through Series A.
Overview
Track the right metrics at the right stage. Focus on unit economics, growth efficiency, and cash management metrics that matter for fundraising and operational excellence.
For every startup analysis, first read universal revenue, unit-economics, and cash metrics; also apply its SaaS section when relevant.
Marketplace Metrics
GMV (Gross Merchandise Value)
Total Transaction Volume:
GMV = Σ (Transaction Value)
Growth Rate:
GMV Growth Rate = (Current Period GMV - Prior Period GMV) / Prior Period GMV
Target: 20%+ MoM early-stage
Take Rate
Take Rate = Net Revenue / GMV
Typical Ranges:
- Payment processors: 2-3%
- E-commerce marketplaces: 10-20%
- Service marketplaces: 15-25%
- High-value B2B: 5-15%
Marketplace Liquidity
Time to Transaction How long from listing to sale/match?
Fill Rate % of requests that result in transaction
Repeat Rate % of users who transact multiple times
Benchmarks:
- Fill rate > 80% = Strong liquidity
- Repeat rate > 60% = Strong retention
Marketplace Balance
Supply/Demand Ratio: Track relative growth of supply and demand sides.
Warning Signs:
- Too much supply: Low fill rates, frustrated suppliers
- Too much demand: Long wait times, frustrated customers
Goal: Balanced growth (1:1 ratio ideal, but varies by model)
Consumer/Mobile Metrics
Engagement Metrics
DAU (Daily Active Users) Unique users active each day
MAU (Monthly Active Users) Unique users active each month
DAU/MAU Ratio
DAU/MAU = DAU / MAU
Benchmarks:
50% = Exceptional (daily habit)
- 20-50% = Good
- < 20% = Weak engagement
Session Frequency Average sessions per user per day/week
Session Duration Average time spent per session
Retention Curves
Day 1 Retention: % users who return next day Day 7 Retention: % users active 7 days after signup Day 30 Retention: % users active 30 days after signup
Benchmarks (Day 30):
40% = Excellent
- 25-40% = Good
- < 25% = Weak
Retention Curve Shape:
- Flattening curve = good (users becoming habitual)
- Steep decline = poor product-market fit
Viral Coefficient (K-Factor)
K-Factor = Invites per User × Invite Conversion Rate
Example: 10 invites/user × 20% conversion = 2.0 K-factor
Benchmarks:
- K > 1.0 = Viral growth
- K = 0.5-1.0 = Strong referrals
- K < 0.5 = Weak virality
B2B Metrics
Sales Efficiency
Win Rate
Win Rate = Deals Won / Total Opportunities
Target: 20-30% for new sales team, 30-40% mature
Sales Cycle Length Average days from opportunity to close
Shorter is better:
- SMB: 30-60 days
- Mid-market: 60-120 days
- Enterprise: 120-270 days
Average Contract Value (ACV)
ACV = Total Contract Value / Contract Length (years)
Pipeline Metrics
Pipeline Coverage
Pipeline Coverage = Total Pipeline Value / Quota
Target: 3-5x coverage (3-5x pipeline needed to hit quota)
Conversion Rates by Stage:
- Lead → Opportunity: 10-20%
- Opportunity → Demo: 50-70%
- Demo → Proposal: 30-50%
- Proposal → Close: 20-40%
Metrics by Stage
Pre-Seed (Product-Market Fit)
Focus Metrics:
- Active users growth
- User retention (Day 7, Day 30)
- Core engagement (sessions, features used)
- Qualitative feedback (NPS, interviews)
Don't worry about:
- Revenue (may be zero)
- CAC (not optimizing yet)
- Unit economics
Seed ($500K-$2M ARR)
Focus Metrics:
- MRR growth rate (15-20% MoM)
- CAC and LTV (establish baseline)
- Gross retention (> 85%)
- Core product engagement
Start tracking:
- Sales efficiency
- Burn rate and runway
Series A ($2M-$10M ARR)
Focus Metrics:
- ARR growth (3-5x YoY)
- Unit economics (LTV:CAC > 3, payback < 18 months)
- Net dollar retention (> 100%)
- Burn multiple (< 2.0)
- Magic number (> 0.5)
Mature tracking:
- Rule of 40
- Sales efficiency
- Pipeline coverage
Metric Tracking Best Practices
Data Infrastructure
Requirements:
- Single source of truth (analytics platform)
- Real-time or daily updates
- Automated calculations
- Historical tracking
Tools:
- Mixpanel, Amplitude (product analytics)
…
FAQ
What are vanity metrics?
Vanity metrics are numbers that look good but don't show the true business performance, like total users without knowing if they continue using the product.
How many metrics should I track?
It's better to focus on 5-7 core metrics that truly matter for your business, rather than trying to track many metrics loosely.
Why are unit economics important?
Unit economics, like CAC and LTV, are crucial because they show your business's financial health per customer, helping you make smarter decisions about growth and investment.
Details
Original description by the author
Track, calculate, and optimize key performance metrics for SaaS, marketplace, consumer, and B2B startups from seed through Series A, including unit economics, growth efficiency, and cash management. Use this skill when defining a metrics framework, calculating CAC/LTV/burn multiple, benchmarking business health, or preparing metrics dashboards for investors or board reporting.
repo: wshobson/agents
Popularity
- GitHub stars
- 41k
- Ranking position
- #4962
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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